Demixed PCA (dPCA) is a linear dimensionality reduction technique that can help to automatically discover and highlight the essential features of complex population activities. The population activity is decomposed into a few components that capture most of the variance in the data and that highlight the dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc.

D Kobak+, W Brendel+, C Constantinidis, CE Feierstein, A Kepecs, ZF Mainen, X-L Qi, R Romo, N Uchida, CK Machens
Demixed principal component analysis of neural population data
eLife 2016, https://elifesciences.org/content/5/e10989
(arXiv link: https://arxiv.org/abs/1410.6031)

Python or MATLAB code for dPCA is available at https://github.com/machenslab/dPCA